Classification of Transcranial Doppler Signals Using Artificial Neural Network

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Journal of Medical Systems [joms] pp682-joms-455334 November 21, 2002 11:1 Style file version June 5th, 2002

Journal of Medical Systems, Vol. 27, No. 2, April 2003 ( C© 2003)

Classification of Transcranial Doppler SignalsUsing Artificial Neural Network

Selami Serhatlioglu,1 Fırat Hardalac,2 and Inan Guler3,4

Transcranial Doppler signals, recorded from the temporal region of brain on 110patients were transferred to a personal computer by using a 16-bit sound card. Thefast Fourier transform (FFT) method was applied to the recorded signal from eachpatient. Since FFT method inherently can not offer a good spectral resolution at jetblood flows, it sometimes causes wrong interpretation of transcranial Doppler signals.To do a correct and rapid diagnosis, transcranial Doppler blood flow signals werestatistically arranged so that they were classified in artificial neural network. Backpropagation neural network and self-organization map algorithms of artificial neuralnetwork were used for training, whereas momentum and delta–bar-delta algorithmswere used for learning. The results of these algorithms were compared in the case ofclassification and learning.

KEY WORDS: transcranial Doppler; FFT; artificial neural network; backpropagation neural network;self-organization map.

INTRODUCTION

The clinical introduction of transcranial Doppler (TCD) has enabled continuousmonitoring of cerebral perfusion in a variety of circumstances. TCD is a noninvasivetechnique that uses a low frequency (2–4 MHz) pulsed sound wave to measure bloodflow velocity within arteries of temporal region of brain. By using spectral analysis ofthe frequency shifts (Doppler effect) that resulted from insonation of blood movingthrough a preselected arterial sample volume, TCD devices can calculate and displaythe peak systolic diastolic velocities, mean velocity, and the resistive index of bloodflow within the interrogated vessel.

Diagnoses made with TCD are based on the detection of increased or de-creased blood flow velocity, absence of blood flow, or changes in resistivity.(1) During

1Department of Radiology, Faculty of Medicine, Fırat University, Elazıg, Turkey.2Department of Biophyscs, Faculty of Medicine, Fırat University, Elazıg, Turkey.3Department of Electric and Electronic, Faculty of Technical Education, Gazi University, 06500Teknikokullar, Ankara, Turkey.

4To whom correspondence should be addressed.

205

0148-5598/03/0400-0205/0 C© 2003 Plenum Publishing Corporation

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206 Serhatlioglu, Hardalac, and Guler

cardiopulmonary bypass, TCD has also been shown to be an effective monitor forthe detection of cerebral emboli, and has been able to confirm the relationship be-tween embolic load and postoperative cognitive impairment.(2) Absolute flow ve-locity varies with age, hematocrit, and regional metabolic activity,(3) thus there is aninconstant relationship between cerebral blood flow velocity and cerebral perfusion.Relative changes in middle cerebral artery (MCA) blood flow velocity correlate wellwith changes in cerebral blood flow (CBF), however.(4)

Artificial Neural Network (ANN) is a mathematical model consisting of a num-ber of highly interconnected processing elements organized into layers, the geometryand functionality of which have been likened to that of the human brain. The ANNmay be regarded as possessing learning capabilities in as much as it has a naturalpropensity for storing experimental knowledge and making it available for lateruse.(5–8) By virtue of its parallel distribution, an ANN is generally well and capa-ble of solving nonlinear problems. A blood vessel, it is diseased or healthy, may beregarded as an inherently nonlinear system due to the absence of the property offrequency preservation as required by the definition of a linear system.(9,10) Appli-cations of ANNs in the medical field include photoelectric plethysmography pulsewaveform analysis(11) and differentiation of assorted pathological data;(12) however,neural network analysis of Doppler shift signals is a relatively new approach.(13,14)

Transcranial Doppler signals that are used in this study are nonstationary sig-nals and therefore these signals have nonlinear properties. The nonstationary TCDsignals are modified as the stationary signals by using discrete nature of fast Fouriertransform (FFT). The spectral curves obtained from FFT analysis are recognized inbackpropagation neural network (BPNN) and self-organization map (SOM) of ANNso that correct and rapid diagnosis of transcranial Doppler signals are achieved.

MATERIAL AND METHODS

The data acquisition system used in this study consist of four main blocks asillustrated in Fig. 1. There are 2-MHz transcranial Doppler unit (Multi DopplerTransducer XX, DWL, GmbH, Uberlingen, Germany), an analog/digital (A/D) in-terface board, a PIII 600-MHz microprocessor-based personal computer with printer.The analog Doppler unit can work both continous and pulse wave mode. The samplevolume sites can be adjusted according to intracerebral artery diameter for recordingdifferent blood flows. Meusurement angle is 60◦.(15)

The signal at the output of the analog Doppler unit is sampled and digitized into16-bit data packets by using an A/D interface board. The digital data are then storedon the hard disk of the PC. The interface board offers a range of sampling frequencies.

Fig. 1. Schematic diagram of the data acquisition system.

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Classification of Transcranial Doppler Signals Using Artificial Neural Network 207

Fig. 2. Construction of frames from Dopplersamples.

The data stored as a sound file on the hard disk of the PC are converted into a textfile by using a simple algorithm implemented in C programming language. Spectralcurves are then obtained from this text file using spectral analysis software developedin MATLAB R12, a powerful software package for doing mathematics by computer.

Analysis of the Doppler Signals

The best way to display the Doppler data information is to perform a full spectralanalysis and present the results in the form of a spectral curve. In spectral analysis ofthe Doppler signal, the time domain Doppler signal is sampled at an appropriate rate.These time samples are grouped as frames, each frame containing the same numberof samples as shown in Fig. 2. The most commonly used frame lengths are 64, 128, and256 samples per frame. Especially in real time spectral curve studies, it is importantto choose the frame length as an integer power of 2 because the performance of theFFT method is better for these frame lengths. For each frame, spectral power density(SPD) functions showing the distribution of signal power with respect to frequencyare obtained by using spectral analysis.

In a spectral curve, the horizontal axis represents time (t or frame number), thevertical axis frequency ( f ) and the gray level intensity at coordinates (t , f ) denotessignal power at frequency f and time instant t . The darker the gray level at coordinates(t , f ), the higher the power of the frequency component f measured at time instant t .By monitoring the spectral curve, variation of the spectral properties of the Dopplersignal and a number of extents related to the blood flow can easily be tracked. Beforeperforming a spectral analysis operation on a Doppler signal, some questions shouldbe discussed: What is the frequency range of the signal? What frame length should beused to estimate a SPD function? How many frequency components are required?

The frequency content of the signal will determine the sampling rate to beused, since the maximum frequency ( fm) analyzed is half of the sampling frequency( fs). Since fm is usually not known a priori and depends upon the vessel on whichthe measurements are performed, it is useful to implement the system such thatit is capable of operating at various sampling frequencies. In the system, samplingfrequencies are 2.56, 5.12, 10.24, and 20.48. These sampling frequencies are relatedto Doppler frequencies from 1.28 to 10.24 kHz, which are the expected frequenciesfor brain blood flow measurements. In this work, measurements were performed atthe sampling frequency of 20.48 kHz, so that the frequency aliasing, in the case ofstenosis, would be avoided.

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The number of signal samples required to form a frame depends heavily onhow stationary the signal is. In general, the Doppler signal is nonstationary. The sig-nal may be assumed to be stationary for 10 ms or greater time periods if the flowis laminar and the velocity is not very high. However, this assumption is not validfor high velocity turbulence flows (jet flow), such as encountered at the stenosisand strain at the artery of brain, where the transcranial Doppler spectrum changesvery rapidly. In this case, the frame length should be shortened to validate theabove assumption. On the other hand, very short frame lengths may yield sta-tistically poor spectral resolution. Therefore, selection of frame length is an im-portant factor in Doppler spectral analysis. The frame length used in thisstudy is 128.

Artificial Neural Network (ANN)

FFT analyzed blood velocity parameters are statistically classified as systole,diastole, resistive index, and standard deviation. These parameters are then appliedto ANN as training and testing data. Also, these parameters are considered as neuronsin ANN.

The neurons in a feedforward neural network are organized as a layered struc-ture and connected in a strictly feedforward manner. The structure of a basic feed-forward neural network is presented in Fig. 3. The feedforward neural network isone of the most widely used ANNs. A great number of successful applications of thistype of network have been reported.(16)

Although there is only one hidden layer in the network, there can be more thanone hidden layer in feedforward neural networks. Each neuron receives a weightedsum from each neuron in the preceding layer and provides an input to each neuronof the next layer. The activation of each neuron is governed by threshold function.In order to train the network, a popular training algorithm for the multilayer per-ceptron (MLP) is the backpropagation method where the error calculated at theoutput of the network is propagated through the layers of neurons to update theweights.(16)

Fig. 3. Basic feedforward neural network structure.

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The backpropagation is basically a gradient descent algorithm. One way to in-crease the learning rate without leading to oscillation is to modify the backpropaga-tion algorithm by including the momentum term.

Another learning algorithm used in this work is delta–bar-delta. The delta–bar-delta algorithm is a heuristic approach for improving the speed of convergence ofthe connection weights in MLPs.

The prediction performance is evaluated using the following statistical metrics,namely, the Normalized Mean Squared Error (NMSE), Mean Squared Error (MSE),Mean Absolute Error (MAE), Min Absolute Error, and Max Absolute Error. NMSEand MAE are the measures of the deviation between the actual an predicted values.The smaller the values of NMSE and MAE, the closer are predicted TCD signalvalues to the actual values.

Another method to measure the importance of an input is to measure the changeof MSE when that input is deleted from the neural network.(17) The size of the MSEcan be used to determine how well the network output fits the desired output. The stopcriteria for supervised training is usually based on the MSE. Most often the trainingis set to terminate when the MSE drops to some threshold. Another approach is toterminate when the change in the error between epochs is less than some threshold.

The correlation coefficient (r) is confined to the range (−1, 1). When r = 1, thereis a perfect positive linear correlation between x (output values deal with ANN) andd (desired values), that is, which means that they vary by the same amount. Whenr = −1, there is a perfectly linear negative correlation between x and d, that is,they vary in opposite ways (when x increases, d decreases by the same amount).When r = 0, there is no correlation between x and d, that is, the variables are calleduncorrelated. Intermediate values describe partial correlation.

At the same time, self-organization map is used for training of TCD data inthis work. The self-organization neural networks is called topology-preserving maps.Although this property is observed in the brain, but it is not found in other ANN.

The weight vector for a cluster unit serves as an exemplary of the input patternsassociated with that cluster. During the self-organization process, the cluster unitwhose weight vector matches the input pattern most closely is chosen as the winner.The winning unit and its neighboring units are not, in general, close to the inputpattern.(18)

RESULTS AND DISCUSSION

FFT analysis is applied to recorded transcranial Doppler signals. The spectralcurves are constructed as shown in Fig. 4. These spectral curves are obtained by using120,000 samples. Each spectral curve is obtained as off-line in 6 s. When the spectralcurves in Fig. 4(a) examined, it is seen that the first peak (systole) occurs at just themitral valve opens by forcing the pressure of left atrium. Since the blood flows fromleft atrium to left ventricule the pressure of the left atrium decrease so that the bloodvelocity decreases. This causes a decrease on the amplitude of the spectral curve. Thispoint is formed as a valley between two peaks. After all these events, the M-shapedspectral curve is obtained as shown in Fig. 4(a). When the slope between systole and

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Fig. 4. (a) Spectral curve of a 32-year-old patient whose bloodflow is normal; (b) Spectral curve of a 60 year-old patient whoseblood flow is abnormal.

diastole is small on the envelope of spectral curve of patients such as aneurysm, thismeans a smearing on the envelope of M-shaped spectral curve as shown in Fig. 4(b).

Standard deviation of systole, diastole, and resistive index, which forms thespectral curve were grouped statistically in Table I. If resistive index is over 0.60 foradults (12>) and over 0.70 for children (12<), it is known as abnormal.(19)

Momentum and delta–bar-delta learning techniques of BPNN and SOM algo-rithms are used for the best learning and training and classification in ANN for TCDsignals.

The data used in this work were diagnosed earlier by the experts using magneticresonance imaging method. The expert results show that 20 patients of 110 were

Table I. Statistical Values of Patients

Patient Number of Patient Systole Diastole Resistivegroup no. patient age group (cm/s) (cm/s) SD index SD Symptom

1 74 Adults 200 100 ±22 0.50 ±0.0609 Normal2 16 Adults 240 90 ±24 0.62 ±0.0308 Abnormal3 16 Children 215 105 ±23 0.51 ±0.0765 Normal4 4 Children 220 60 ±27 0.72 ±0.0168 Abnormal

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Tabl

eII

.P

erfo

rman

ceV

alue

sof

SOM

and

BP

NN

Alg

orit

hms

Rel

ated

Wit

hD

elta

–Bar

-Del

taan

dM

omen

tum

Lea

rnin

gTe

chni

ques

Per

form

ance

SOM

delt

a–ba

r-de

lta

SOM

mom

entu

mB

PN

Nde

lta–

bar-

delt

aB

PN

Nm

omen

tum

Abn

orm

alN

orm

alA

bnor

mal

Nor

mal

Abn

orm

alN

orm

alA

bnor

mal

Nor

mal

MSE

0.17

8113

0.18

2214

0.09

4863

0.10

0292

0.09

437

0.09

5174

0.06

4027

0.06

4253

NM

SE0.

8444

40.

8638

790.

4497

490.

4754

850.

4474

120.

4512

220.

3035

550.

3046

26M

AE

0.22

3753

0.22

8943

0.17

6428

0.18

4331

0.17

7482

0.18

0738

0.14

3674

0.14

7681

Min

Abs

olut

eE

rror

0.00

7097

0.00

691

0.00

404

0.00

7442

0.00

9703

0.00

9686

0.00

0867

0.00

6704

Max

Abs

olut

eE

rror

1.05

5427

1.05

5424

1.03

4542

1.02

8306

1.02

9669

1.01

7497

1.00

4418

0.98

8921

r0.

5589

550.

5589

250.

7503

210.

7379

620.

7483

930.

7512

630.

8356

740.

8402

19P

erce

ntco

rrec

t69

.230

7786

.666

6684

.615

3990

.084

.615

3996

.666

6692

.307

6996

.666

66

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Fig. 5. BPNN algorithm and MSE curve of momentum learningtechnique.

abnormal. Delta–bar-delta and momentum learning techniques obtained from 500epoch with BPNN and SOM algorithms of ANN and test performances are given inTable II. On the other hand, performance of SOM and BPNN algorithms of delta–bar-delta and momentum learning techniques are also given in Table II.

BPNN momentum learning technique has statistically learned with less errorthan delta–bar-delta learning technique for abnormal data in the case of MSE 32%,NMSE 32%, MAE 19%, Min Absolute Error 91%, and Max Absolute Error 2%;for normal data in the case of MSE 32%, Normalized Mean Squared 32%, MAE18%, Min Absolute Error 30%, and Max Absolute Error 3%.

BPNN momentum learning technique has statistically learned with less errorthan SOM momentum learning technique for abnormal data in the case of MSE33%, NMSE 33%, MAE 18%, Min Absolute Error 32%, and Max Absolute Error2%; for normal data in the case of MSE 36%, Normalized Mean Squared 36%, MAE19%, Min Absolute Error 10%, and Max Absolute Error 4%.

Table III. Estimation Values of ANN

Output values deal with ANN Desired values

Systole Diastole Resistive index Normal Abnormal Normal Abnormal

230 90 0.608696 1.006745696 −0.00941777 1 0231 161 0.30303 0.135806143 0.865901947 0 1192 150 0.21875 0.014649399 0.983849645 0 1211 80 0.620853 0.984740138 0.01803726 1 0210 161 0.233333 0.051639147 0.948316336 0 1230 68 0.704348 1.026445389 −0.03178186 1 0220 171 0.222727 0.072472461 0.928698301 0 1200 130 0.35 0.065958731 0.934093118 0 1210 130 0.380952 0.105992228 0.894643486 0 1235 56 0.761702 1.029545903 −0.03776388 1 0

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BPNN momentum learning technique has statistically learned with less errorthan SOM delta–bar-delta learning technique for abnormal data in case of MSE64%, NMSE 64%, MAE 35%, Min Absolute Error 87%, and Max Absolute Error5%; for normal data in the case of MSE 65%, Normalized Mean Squared 65%, MAE35%, Min Absolute Error 2%, and Max Absolute Error 6%.

At the same time, correlation coefficient r in BPNN momentum technique is be-tween 0.83 and 0.84. These values are close to 1, so that BPNN momentum techniquecan perform the classification 8% succesfull in the case of abnormal data comparingwith delta–bar-delta. On the other hand, SOM momentum tecnique can perform theclassification 7% succesfull for normal data comparing with SOM delta–bar-deltalearning technique.

The MSE curve of BPNN momentum learning technique is obtained at 500 stepsas shown in Fig. 5. The figure shows that BPNN momentum learning technique islearned blood flow values less than 0.1 error after 50 steps.

In order to test the system whether it is succesfull or not, the FFT analyzedunknown brain blood flow signal of 10 patients are statistically obtained. Then, thesedata were classified using BPNN momentum learning technique that involved infeedforward ANN. Table III shows these results.

As it is seen in Table III, when the output values calculated by ANN are com-pared with the desired values, it is observed that the classification is 100% succesfull.As a result of this classification, it is calculated that blood flow velocity of six patientis abnormal and four patient is normal.

CONCLUSION

We have worked on 110 patients. TCD signals of 90 patients are normal, 20of them are abnormal. When BPNN momentum delta–bar-delta learning techniqueare compared with SOM momentum delta–bar-delta learning technique, the BPNNmomentum delta–bar-delta learning technique has been learned with less error whileit requires less steps.

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Classification of Transcranial Doppler Signals Using Artificial Neural Network